Using Regression Analysis to Improve Cause and Effect Analysis

By Shmula Contributor, Last Updated January 21, 2018

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Cause and effect analysis is a great way to study the chain of events that led to specific developments in your process, and it can get as detailed as you want it to be, in order to give you a complete overview of the situation. However, by itself, it doesn’t give you the full picture that you could obtain by combining it with other popular methodologies. Regression analysis is a good candidate for that, and it’s a great way to improve the results you get from your cause and effect analysis.

Regression analysis allows you to identify the exact relationships between variables, and to see how changing one variable affects the system as a whole, so it shouldn’t be hard to see the connection between it and cause and effect analysis. It can allow you to get a more focused overview of the way different inputs affect the final output, and it can be a great way to optimize your work and apply cause and effect analysis even more efficiently.

Streamlining Your Work Through Effective Analysis

One of the most common ideas in lean and Six Sigma is to minimize waste as much as possible, and ensure that your resources are being used in an optimal way. There are many tools available to work in that direction, and cause and effect analysis can actually be incredibly useful for that specific purpose. When you’re working with regression analysis in combination with it, you’ll know exactly how specific variables impact the quality of the output.

More importantly, you’ll know which variables have no effect at all, at least when it comes to the important parts of the output. This makes it easy to identify areas which would be a waste of effort if you’re trying to optimize the process. On the contrary, it will show you where you need to be more active and think more deeply about how the process can be changed.

Fine-Grained Analysis

Regression analysis will also allow you to prioritize the factors uncovered in cause and effect analysis, and to know which ones have a bigger impact on your operations in their current state. This addresses a well-known flaw in cause and effect analysis, as the technique is sometimes not ideal for sorting the different variables by their importance. When you’re working with regression analysis on the side, you can get a much more informative overview of how things are related at the moment.

Keep in mind that you don’t have to dig that deeply – regression analysis is simply a tool that allows you to optionally take a closer look at the situation. Sometimes it will turn out that it’s not actually necessary to even use it in combination with cause and effect analysis, as it may already give you all the information you need alone. But for situations where that’s not the case, and you do need to examine the state of things more closely, this is easily the handiest tool you’ll find available.

Let’s recap:

When performing a cause and effect analysis, the team brainstorms POTENTIAL factors that effect your output.

Cause and effect analysis is a great way come up with ideas on where to focus your effort, in order to prevent further problems from developing. But its true power tends to shine when combined with regression analysis, which allows you to take a more precise look at the way things work, and to figure out and prioritize the intricate statistical relationships between variables. You don’t always have to use the two together like that, but it can definitely help in some cases, and learning to recognize them is a skill you’ll want to master as quickly as possible.